Speakers of a language routinely infer many facts from the explicit information that is given to them: This ability is in fact crucial to achieving communication efficiency.

the solution proposed in that paper involves linking both distributions and traditional intension to mental concepts, using an F-first FDS.


The distributional vectors of negated items and their alternatives (either noun or composed phrase vectors) were also fed to a supervised regression algorithm, in order to tune similarity to the specific factors that determine felicitous alternativehood.

we assume that part of the meaning of a linguistic expression is given by a probability distribution over possible linguistic expressions that might follow.


Mitchell and Lapata (2008) introduce an evaluation that has been adopted by a number of other researchers.

for example, the WordNet hyponyms for building include the synset in Example (3).


These methods are based on the Categorial framework, as is the Frobenius algebra baseline; however, the methods described here are all implemented with Skip-Gram vectors, and matrices and tensors learned by linear regression.

it seems unlikely that vector addition can achieve a MAP much higher than its current one of around 0.5.


In many cases the SVO triple represented a property which, although it could be true of the term, did not distinguish it from other terms, especially from other hyponyms of the same head noun.

the two annotators originally worked together on a small set of data.


Only terms with at least four good properties, and head nouns with at least four terms, were retained after this step.

the head noun does contribute to the composition of relative clause meaning, as we can see from the fact that on the test data, PLF achieves a significantly higher MAP than VArg.


We also adopt the modification of Gupta, Utt, and Padó (2015), in which the word vector for the verb is not added to the composition result, because this formulation was found to yield more accurate results.

person that hotel accommodates is a good property, because, although hotels might accommodate other people (e.g., for meetings or in a restaurant), their main and canonical purpose is to accommodate travelers.


To some extent, we answer this in our translation of our DS composition back into TCL functors.

we show that such methods can furnish the appropriate TCL functors, provided we take one big step: we identify TCL's internal content with vectors, which distributional methods use to represent word meaning.


It is unclear to us how they actually exploit this disambiguation that they informally describe in inference.


In the case of AN composition, all of the intersections were zero for this setting, making it impossible to compute a correlation.

finally, note that whereas INT has produced high quality neighbors in these examples where only two words are composed, using INT in the context of the composition of an entire sentence would tend to lead to very sparse representations.


In order to calculate similarities, contexts are weighted using the variant of PPMI advocated by Levy, Goldberg, and Dagan (2015), where cds is applied with α = 0.75.

accordingly, Turney explicitly uses a different procedure where a separate Spearman's ρ is calculated between the model scores and the scores of each participant.


The string τ must, therefore, not only encode the sequence of dependency relations appearing along the path, but also whether each edge is traversed in a forward or backward direction.

the CCG formalism was also used by Hermann and Blunsom (2013) as a means for incorporating syntax-sensitivity into vector space representations of sentential semantics based on recursive auto-encoders (Socher et al.


Instead, separate points are created for each human annotator, as discussed in Section 5.3.1.

this APT can be seen as a characterization of the distributional properties of the verbs that nouns that dry/JJ modifies can take as their direct object.


The procedure is discussed at length in Turney (2012), who argues that this method tends to underestimate model performance.

all occurrences within the string of either rr or rr for r ∈ R are replaced with , and this process is repeated until no further reductions are possible.


As a reminder, we probabilistically test for entailment by computing the probability of the query given the text, or, more precisely, P(H|T, KB, W T,H ).

samplesearch is an importance sampling algorithm that has been shown to be effective when there is a mix of probabilistic and deterministic (hard) constraints, a fundamental property of the inference problems we address.


The complex query formula H is added to the MLN using the hard formula where result(D) is a new ground atom that is not used anywhere else in the MLN.

still, there are many ways our lexical classifier could be improved, even using all of the features in the system.


Converting this disjunction to clausal form becomes increasingly intractable as the number of variables and constants grow.

it seems to do particularly poorly on antonyms (strike miss), and items that require additional world knowledge (surfer → man).


These pairs include note → paper, swimmer → racer, eat → bite, and stand → wait.

we use first-order logic as a basic representation, providing a sentence representation that can be easily interpreted and manipulated.


The new evidence ogre(D) prevents the query from being judged true given T 1 .

gogate and Domingos (2011) show that to calculate the probability of a formula H given a probabilistic knowledge base KB, it is enough to compute the partition function Z of KB with and without H added as a hard formula: all we need is an appropriate algorithm to estimate the partition function Z of a Markov network.


Apparently, eclassif already captures all the useful rules that we were getting from ppdb.

logical and distributional approaches clearly differ in the strengths that they currently possess (Coecke, Sadrzadeh, and Clark 2011;Garrette, Erk, and Mooney 2011;Baroni, Bernardi, and Zamparelli 2014).


We refer the reader to the provided references for further information.

apart from spanning a variety of languages, these data sets allow us to see the influence of annotation criteria on the coverage of different restrictions on non-projectivity.


The need for each task is discussed, together with an overview of work that has developed specialized methods for social media-type texts.

there are some references to this line of work but the majority of the content remains unexplored.


She is interested in natural language processing and machine learning techniques to understand both how people use language and build language-related technology.

a reflection on the types of techniques that work well so far, and insights into new types of models proposed for social media, and an emphasis on methods addressing special properties, for example, streaming algorithms, would have been worthwhile additions.


For instance, two subject-sharing relations may be realized in the same clause.

in this section, we illustrate by means of examples how our approach accounts for various factors impacting sentence segmentation, aggregation, and surface realization.


All features are derived from the input semantics, that is, a sequence of relations and concepts.


Our approach falls within these two lines of research; it uses a graph structure to model the geometry of the data points (the words in a text) and a knowledge base to extract the senses of each word and to compute the similarity among them.

in fact, the mentions are not explicit and require the use of common knowledge to identify their intended meaning.


this behavior is not surprising because the target words of S10 belong to a specific semantic domain.


Furthermore, we evaluated our approach on two data sets, SemEval-2013 task 12 (S13) (Navigli, Jurgens, and Vannella 2013) and KORE50 (Hoffart et al.

we use WordNet to find alternative lexicalizations of a lemma, choosing the one that co-occurs more frequently with the words in its context.


We often discuss concepts such as democracy or dictatorship using mechanism terminology, and thus a distributional learning approach would learn that they share features with political systems (from their literal uses), as well as with mechanisms (from their metaphorical uses, as shown next to the respective graph edges in the figure).

they ran their system on these data, and output a ranked set of metaphorical examples.


A metaphor processing component could help to avoid such errors.

the latter is more appropriate for concrete concepts, and a more flexible representation is needed to model abstract concepts.


Unfortunately, solving this minimization problem is NP hard (Wagner and Wagner 1993;Von Luxburg 2007).

the results that came out of these studies suggest that in order to reliably capture the patterns of the use of metaphor in the data on a large scale, one needs to address conceptual properties of metaphor, along with the surface ones.


The TS constrained clusters exhibit the same trend with respect to cluster diversity.

this cluster models the latter meaning of passage.


Finally, we proposed a computational model based on machine learning for identifying argument components (Section 5.1).

we will discuss the phenomena of narratives in argumentation in more detail later in Section 4.4.10.


A detailed discussion of these different perspectives can be found in Blair (2004), Johnson (2000), Reed and Walton (2003), Micheli (2011), O'Keefe (1982, and Rapanta, Garcia-Mila, and Gilabert (2013).

stab and Gurevych (2014a) annotated 90 argumentative essays (about 30k tokens), annotating claims, major claims, and premises and their relations (support, attack).


29 However, only 4% of the documents in our data set contain arguments for both sides of the issue.

having an argument as a product within the argumentation process, we should now define it.


To do so, certain common-ground knowledge is required.

the overall Macro-F 1 score is 0.602 (accuracy = 0.754).


• Dr. Smedley believes that homeschoolers have superior socialization skills, and his research supports this claim.

the creation of the data set in terms of annotation guidelines and reliability is not reported-these limitations, as well as its rather small size, have been noted (Feng and Hirst 2011).


Comparably low R2 values (observed in SAX*, K-means, and baseline clusters) demonstrate one of the main claims of our methodology: hashtag similarity is timerelated.

table 2 shows average and standard deviation (SD) of NIG and Precision over the 60 clusterings C W i of Summer2014.


This is considerably larger than the 250 million tweets of the Twitter 2013 collection, 9 which was, to the best of our knowledge, hitherto the largest available collection used in micro-blog analysis.

hashtags' sense similarity and dissimilarity is inferred from the context in which they appear.


despite substantial progress in the field, implicit topic/aspect identification as well as complex explicit aspects (which use, for example, verbal constructions) remain unsolved.


hunston and Thompson (2000a), in their Introduction, propose that there are two aspects to evaluative language: modality and something else, which is variously called evaluation, appraisal, or stance.


Not enough research has explored exactly how evaluative expressions are affected in the presence of nonveridical operators.

the outcomes did not result in a significant improvement.


After an introduction to evaluation and subjectivity in linguistics, we now present research in computational linguistics, starting with a basic problem definition and a common characterization of evaluative language.

in what Biber and Finegan call Faceless texts, a high proportion of evaluative expressions is more significant, and is probably indicative of an overtly subjective text (i.e., one with a higher "volume," from the point of view of evaluative language).


One may think that the topic is a bit out of the focus for the book.

the level of clarity and conciseness throughout the book demonstrates that the authors have a clear and deep understanding of these areas.


We then compare how often each signal ranks the correct translation higher than the other signals.

whereas the correct translation has a good temporal match (sim temp (terremoto, earthquake) = 2 • 10 −4 ), the non-translations are less temporally similar (sim temp (terremoto, microsoft) = 2 • 10 −5 , sim temp (terremoto, strength) = 3 • 10 −5 ).


However, this strategy only generates states that result from a history of correct parsing actions.

by sampling from the predicted distribution, we are effectively increasing the chance of straying from the gold path during training, while still focusing on mistakes that receive relatively high parser scores.


Transforming the sampling distribution towards preferring less probable events (α = 0.75) is especially beneficial when training with pretrained word embeddings.

the REDUCE-LEFT transition pops the top of the stack and attaches it as a child of the first item in the buffer.


Our unsupervised system outperforms all the semi-supervised and supervised systems that participated in NEWS10 on three language pairs.

table 5 shows examples of word pairs that are wrongly annotated as transliterations in the gold standard.


The behavior is similar to the unsupervised system.

correspondingly, nontransliteration pairs are assigned a lower probability by the transliteration sub-model compared to the non-transliteration sub-model.


In the English/Hindi data set, such word pairs are mostly English words that are borrowed in Hindi like the word "calls," which is translated in Hindi as "cA:llaen."

we use the unlabeled data to help align it correctly.


59.0) words per narrative, and the control participants produced an average of 114.4 (s.d.

a rounded estimate of 0 signifies non-TIB and a rounded estimate of 1 signifies TIB.


These answer justifications are in the form of text aggregation graphs (TAGs), each composed of two to three graphlets produced by the preprocessing step.

for example, for a question to be classed as good, at least one of the six answer justifications must be rated as good.


We then concatenate these three composite vectors, as well as the TAG features and the CR scores, to form the input layer to the network.

other: Although we were able to determine the source for the majority of errors, for 18% of incorrect questions we were unable to readily identify the issue.


7 We will exploit this information in the next section, where we design features to capture the fitness of a TAG as a justification for a given answer.

our knowledge bases do include many negated sentences or phrases that provide a contrast between two categories of items through negation (e.g., melting is a physical change, not a chemical change).


Clearly, one possible way of deciding when to use a relational property is by assessing its impact on identification.

if the redundant information turns out to be difficult to recognize (e.g., star shape), then referential overspecification will have the opposite effect.


After this overview, the chapter focuses more specifically on two existing large-scale LLKBs, namely, Uby and BabelNet, and explains how they complement each other.

chapter 6 considers the usage and benefits of LLKBs in multilingual applications, particularly multilingual semantic relatedness and computer-aided translation.


Automatic text understanding requires knowledge and, so far, machines know only what we give or teach them.

chapter 1 presents lexical knowledge bases at large.


A variety of LKBs are thus presented, from WordNets to FrameNets via Wiktionary and OmegaWiki, and, for each one, information types, main advantages, and typical usages are explained.

two formal definitions are introduced, one of word sense linking, and the other of linked lexical knowledge base.


In TIGER, 52 POS tags and 46 DEPRELs are used.

if the same condition holds for a dependency structure (s, ≤ s ), then we say that (s, ≤ s ) is projective.


Figure 11a) is to extract a recursive partitioning directly from a hybrid tree (s, ≤ s ).

in the remainder of Section 6 we extend the induction pipelines for LCFRS to induction pipelines for LCFRS/sDCP hybrid grammars, as illustrated in Figure 14.


As can be seen from the table, the micro-average for the five chunks is 0.87 (F1-measure), 9 indicating that we are able to get good quality annotation for this task.

with a hierarchically aligned parallel treebank in place, we are ready to semi-automatically extract and categorize the translation divergences between the two languages.


As a result, it has no difficulty bridging translation divergences caused by reordering, category mismatch, or the absence of function words on either side of a language pair.

we also show that HACEPT allows the extraction of syntax-based translation rules, most of which are expressive enough to capture the translation divergences, and point out that the syntactic annotation in existing treebanks is not optimal for extracting such translation rules.


On the dialect atlas, we find that the fraction of statistically significant variables ranges from 55.2% to 78.3%, depending on the statistical approach (Section 5.2).

: we computed Moran's I for cutoffs in the range of 200 to 1,000 miles and selected the cutoff that yielded the lowest p-value.


For example, selecting the minimum p-value across the alternants would mean that the null hypothesis would be more likely to be rejected for variables with more alternants; averaging the p-values across alternants would have the opposite problem.

these tests can incorrectly fail to reject the null hypothesis if the nature of the geolinguistic dependence does not match the underlying assumptions of the test.


This is done during embedding learning, in contrast to our post-processing method.

(i) The column normalization constraint (Equation (35)) will align W 0 and W 1 , as we just split the original word embeddings and add them up again.


Our approach for parsing argumentation structures consists of five consecutive subtasks, depicted in Figure 3.

embedding features are based on word embeddings trained on a part of the Google news data set (Mikolov et al.


For instance, the annotator labeled the entire sentence as a claim although it includes an additional premise.

for finding the bestperforming models, we conduct model selection on our training data using 5-fold crossvalidation.


The following constraints tie the auxiliary variables b ij to the variables x ij : The first constraint ensures that there is a path from i to j represented in variable b ij if there is a direct relation between the argument components i and j.

although humans may be able to deduce the missing parts by interpreting the argument, existing argument mining methods fail on that task and may produce incomplete or even wrong argumentation structures.


We use macro-averaging as described by Sokolova and Lapalme (2009) and report macro precision (P), macro recall (R), and macro F1 scores (F1).

topic and stance identification: We found in our previous annotation study that knowing the topic and stance of an essay improves inter-annotator agreement (Stab and Gurevych 2014a).


Evaluating the agreement at the sentence level is an approximation of the actual agreement since the boundaries of argument components can differ from sentence boundaries and a sentence can include several argument components.


Recently, computational argumentation has been receiving increased attention in computer-assisted writing (Song et al.

it is unlikely that identification errors will significantly influence the outcome of the downstream models when applied to persuasive essays.


Features used for argument component identification (*indicates genre-dependent features).

the results may not be globally consistent.


The categorial disorder δ we want to estimate is the average value taken by D for true pairs only, which formally corresponds to the conditional expectation of D given the event truePair, and is given by Equation (2): In other words, what we really get from an alignment is a "fuzzy set" of true pairs rather than a classical set, and the best estimate of δ we can get from this data is the weighted (by p i ) average value of {δ i }.

this is one of the most important features of γ cat , because it corresponds to the most frequent situations (annotators usually combine different kinds of discrepancies), and proves that this measure is fully focused on categorization.


Indeed, we will see in a further section that size may affect cu α.

in 2004, Obama received national (…) Units of different lengths, but of the same importance (in Named Entity Recognition).


We observed a systematically higher similarity to the discourse trees of good translations, in all the specific elements tested and across all language pairs.

for example, figure 1 in the next section shows discourse trees for three different translations of a source sentence.


Compared with those previous publications of ours, here we provide additional detail and extensive analysis, trying to explain why discourse information is helpful for MT evaluation.

this translation has the same discourse structure and labels as the reference, with the only exception of the top-most discourse relation (Joint vs. Attribution).


For each language pair and corpus year, we micro-average the results for the five most frequent discourse relations.

figure 2 shows that, when applied to discourse trees, the syntactic tree kernel may limit us on the type of substructures that we wish to compare.


To the best of our knowledge, semantic relations between clauses in a sentence and between sentences in a text have not been seriously explored.

for example, the frequency difference for the Elaboration tag between good and gold translation trees is 58, which is smaller than the difference between bad and gold, 323.


When dealing with evaluation metrics based on lexical matching, such as BLEU or NIST, it is easier to understand how and why they work, and what their limitations are.

in the previous two sections we provided a quantitative analysis of which discourse information has the biggest impact on the performance of our discourse-based measure (DR-LEX) and also which parts of the discourse trees help in distinguishing good from bad translations.


Several alternatives emerged to overcome these limitations, most notably TER (Snover et al.

we show that (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference RST tree is positively correlated with translation quality.


The top three evaluation thus provides additional evidence that knowledge of error patterns available in the adapted models is extremely important.

in this article, we consider two groups of closely related languages: Russian and Polish (Slavic), and Spanish and italian (Romance).


We conclude with a discussion of the adaptation approaches, the advantages of each of these algorithms, and situations in which one may be preferred over the other in Section 7.

the matrices also give a sense of the error baseline.


Based on preposition errors in the FCE training data.

when training on native data, increasing the training size is easy.


of the models are indeed different, and that the features encoded by TEXTUAL are more associated with syntactic constructions than in the case of VISUAL.

the strength of association between A i and C can be measured by their mutual information: Similarly to Li et al.


We propose a method for estimating the amount of contribution of individual tokens in the input to the final prediction of the networks.

in English the information structure of a single sentence is expressed via linear ordering: The TOPIC of a sentence appears sentence-initially, and the COMMENT follows.


The most popular semantic similarity evaluation sets such as SimLex-999 or SimVerb-3500 consist of word pairs with similarity ratings produced by human annotators.

a total of 175 concepts were extracted from the McRae feature norms data set (McRae et al.


In summary, the large gap between human and system performances along with the FR superiority over more sophisticated LE approaches from prior work unambiguously calls for the next generation of distributional models tailored for graded lexical entailment in particular.

we acknowledge that this definition is vague, and the actual disambiguation process was left to the annotators and their intuition as native speakers.


For instance, BLEU is based on shared words between the candidate and the reference translation, and gives only a very general indication about quality.

in practice, empirical and expert methods are complementary and both can be combined when one builds a lexical resource, as indicated by the human intervention in Figure 2.


(2015) report significant improvements of the BLEU score for German light-verb constructions, and Pal, Naskar, and Bandyopadhyay (2013) found significant BLEU improvements for English and Bengali multiword named entities.

evaluation of MWE-aware parsers and of whether or not MWE identification helps to improve the parsing quality requires some additional care.


A sentence like He walked by and large tractors passed him cannot be analyzed correctly if by and large is pre-analyzed as a multiword adverb (by_and_large).

their evaluation often requires data sets annotated with semantic compositionality, which are not easy to build.


Lack of Description of Negative Results.

because such models are almost always derived from external language data, it is unclear where to draw the line between closed and open.


So, we would expect that a system able to answer the question "Where is the nearest ER?"

intonation is crucial to getting the interpretation of this story right.


Then, for a given path θ in A of size k: The last equation is because N (0) is the weight accumulated in all paths in the WFA, W A (Θ A (0, |Q| − 1, •)) (as we will see below), and W A (θ) is defined in a way similar to Equation (6).

note that, in Hernando, Crespi, and Cybenko (2005), Mann and McCallum (2007), and Ilic (2011), the computation of Equation (2) is studied, whereas in this article the computation of Equation (3) is studied, as in Grenander (1967).


These values can be efficiently computed with time complexity O(|δ|) following the pseudo-topological order defined on A.

: The inner part in Equation 14is analogous to Equation (9), and it can be interpreted as the addition of the weights of all paths from i to |Q| − 1.


2004) and also to compute G−E for some free ET E, where |G| = def |L| + |H| + |E| + |D|.

because weighted soft constraints generalize hard constraints, finding efficient algorithms for solving systems with hard constraints is a first step toward finding efficient algorithms for finding the highest-scoring solution under weighted constraints.


Again, tree T is a smooth tree decomposition of G, with the same width as T. Furthermore, the replacement of v 2 by v 1 does not affect the bags of T where these nodes have been introduced for the first time.

we have set sequence of 2s transitions γ = t 1 , .


Furthermore, all of these proposals still retain the stack and buffer architecture of the transition-based dependency parsing system they extend.

for each graph, we derive a vertex order corresponding to the English word order by using the automatically generated alignments provided with the data set, which  The distribution of AMR relative treewidth.


Throughout this section, we assume that our semiring of weights K is the semiring of real numbers, with the usual addition and multiplication operations.

7.1.1 Multisets and Languages of Multisets.


We now show that the path language of a recognizable DAG language is always a regular string language.

the first two statements follow directly from the fact that a DAG language is empty (finite) if and only if its binarized counterpart is empty (finite, respectively).


Similarly, entities including John, Mary, and Sue can be generated from one or more states labeled q person , allowing an arbitrary number of instances of coreference.

the m-automata A = (Ξ, Q, τ, s, ρ) appearing in this section are also unweighted.


I wished there was an additional chapter on other applications besides grammar models.

an example is blocked Gibbs sampling using dynamic programming.


It is aimed at a researcher or student who is already familiar with statistical modeling in natural language (i.e., at the level of introductory books such as Manning & Schütze [1999], Jurafsky & Martin [2009]).

i think a reader who completes this book would have gained the technical background to do such a survey by themselves.


The fourth chapter becomes more technical; approaches the crucial problem of metaphor annotation.

i found the last chapter, about applications of metaphor processing, quite thoughtprovoking.


natural language is an intricate object for computers to handle.


For each RST dependency tree, constituency spans are computed from the transitive closure of its adjacency matrix (i.e., its maximal projection).

the notion of head EDU is instrumental to the formal equivalence between c-trees and d-trees we show in Section 3.3.


The source of ambiguity is the alternation between sides.

5 the representations of (complex) discourse units are built from the representations of their head EDUs.


Many old instances misclassified as bridging are definite NPs without further modification and common noun heads without a string match to a previous mention.

the isSameHead feature (table 12: f 13) checks whether antecedent candidates have the same head as the anaphor: this is rarely the case in bridging (except in some cases of set bridging and spatial/temporal sequence, see Example (30)) and can therefore be used to exclude antecedent candidates.


For each mention, α is calculated via its head frequency in the NomBank annotation divided by the head's total frequency in the WSJ corpus on which the NomBank annotation is based.

the anaphor the local council would preferably be interpreted as the council of a village instead of the council of a state due to the occurrence of local.


in this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.


By contrast with displacement calculus D, which is single-conclusioned (it is intuitionistic), and which absorbs the structural properties in the sequent syntax (it has no structural postulates), LG has a non-classical term regime that can assign multiple readings to a proof net, and has semantically non-neutral focusing and defocusing rules.

10 Since DA foc is Cut-free (see Appendix A) we consider Cut-free DA foc proofs of S. Case |S| = 0.


In DA foc Constraint (33) is not imposed.

lambek calculus is continuous (planarity).


This approach can be extended even further by modifying the grammatical formalism into a probabilistic one.

figure 8), the data are less-smoothed for clarity; the shaded area denotes the SE of the smooth.


Furthermore, tokens can be assigned to allomorphs or gender/conjugational variants in each paradigm.

our model is an extension of our previous hierarchical clustering algorithm (Can and Manandhar 2012).


Well-defined paradigms in the lower levels of trees were learned.

our model also outperforms the model from Can and Manandhar (2012) (which we refer to as Single Tree Probabilistic Clustering), although the results are not directly comparable because the largest data set we were able to train that model on was 22K words due to the training time required.


Our model outperforms other unsupervised models in Morpho Challenge 2010 with a F-measure 50.71%.

because of the difficulty in measuring the quality of hierarchical paradigms, which will require a corresponding hierarchically organized gold data set, we are unable to provide an objective measure of the quality of hierarchical structures learned.


We set β s < 1 and β m < 1 in order to generate a small number of stem and suffix types.

we use only the word types (not tokens) in training.


It's hard for me to overstate how profoundly Aravind influenced my career and my life, since he took me on as his PhD student 30 years ago.

aravind described parentheticals, epithets, extraposed predicates, and sentential relatives as "constructions that require a skilled tree surgeon to force a single tree over a sentence."


Hence the usage of BLEU to evaluate NLP systems is only sensible in the presence of validation studies which show that BLEU scores correlate with direct evaluations of the utility of NLP systems.

i accepted papers that included human-written texts as one of the "systems" being evaluated, provided that the study also looked at at least one computer NLP system.


We achieve our best performance on German texts, with only four misclassifications.

similar to the base learners, the meta-classifier can generate both continuous and discrete output.


For example, it may be the case that a certain ensemble element is more accurate at classifying instances of a certain class or there may be interactions or correlations between the outputs of certain elements that could help improve results over a simple fusion method.

the third and final model is a hybrid approach, building an ensemble of meta-classifiers.


We did not use any NLP tools for Norwegian as the corpus we use is already annotated with POS tags.

to the previous methods, this combiner disregards the consensus opinion and instead picks the prediction of the expert with the highest degree of confidence.


Why does LDA outperform other meta-classification methods?

the meta-classifier may learn how the base classifiers commit errors and attempt to correct their biases.


We see that the derivation is composed of a number of smaller fragments (drawn as triangles).

this would imply that an arbitrary instance (G, w) of the universal recognition problem with ε-entries can be converted (in polynomial time) into an equivalent instance (t(G), w) without ε-entries.


We define top(τ) = X and yield(τ) = w L w R , where juxtaposition denotes string concatenation.

at this point, only some rule of type (26) can be applied, resulting in the reduction of the stack, immediately followed by some rule of type (25), which is a composition rule of degree m. If the derivation were to use more than one occurrence of (26), then it would derive a category whose stack contains fewer than m elements.


The category at the root node of this fragment is E(c).

the rule is also thought to be specialized for that token, meaning that the rule contributes to the derivation by introducing some structure representing the syntactic frame and valencies of the token itself.


compounds that are split at entirely incorrect positions constitute the lowest error class.


Next, we apply our method to candidates without any POS filtering and report results for candidates surpassing a frequency threshold of 10.

12 Using this information, we compute the AP for these 1,000 ranked words.


Interestingly, we observe an opposite trend for word2vec.

these mechanisms are intended to rule out spurious parts and "words" that are in fact short abbreviations.


Reducing errors among function words that are observed in the parallel data may help prevent the decipherment model from aligning words that are spuriously similar "false friends" when analyzing the monolingual data.

our model is also very scalable, and can be applied to settings with more data than we experiment with here but still insufficient data for modern embedding-based unsupervised methods.


For French-English, we used the Hansard corpus (Brown, Lai, and Mercer 1991), containing parallel French and English text from the proceedings of the Canadian Parliament.

the goal of decipherment algorithms is to leverage such statistical similarities across languages, and effectively learn word-level translation probabilities from monolingual data.


The accuracies reported here are significantly lower than those achieved by modern supervised methods (and unsupervised methods with large corpora).

this reduced our model's performance (as expected), but we still achieve a higher BLEU score than the baselines.


Probability models for word alignment of parallel data are one of the original problems studied in statistical machine translation .

rather than summing over all possible e 1 e 2 , we can approximate via sampling.


Navarretta and Olsen studied the equivalents of this, that, and it in Danish (455 instances) and Italian (114 instances) written and spoken data.


These constructions have not yet received much attention in computational approaches, and we do not discuss them in the rest of the article.

the system's overall performance is 22.2 F 1 -score (P 22.6, R 21.8), as compared with a baseline of F 1 16.5 (P 15.3, R 17.9).


12 The first instance of it in the example refers to curtseying while you're thinking what to say, which they call extended reference, as the reference is no longer to a person or object but to a whole process or complex phenomenon, and the referent is expressed by a clause or string of clauses instead of a simple noun phrase.

this resulted in a corpus of career-counseling interviews between a professional counselor and graduate students at the university.


Whereas concrete objects are introduced by noun phrases (or, more precisely, by their determiners), eventualities are introduced by finite clauses (or, more precisely, by their inflectional marking).

the anaphor and the (linguistic) antecedent are marked as usual, the semantic antecedent of that is the fact or the proposition or the idea that an engine cannot arrive at Bath in time.


The results regarding individual verbs (i.e., subsets of all data) were much more variable, with deviations of up to 16.7 points.

given that citation counts are highly skewed, we also compared the medians (that are influenced less by outliers).


We thank all authors who took the effort to respond to our request for their data and code.

releasing software that makes it easy to reproduce and modify experiments should be an essential part of the publication process, to the point where we might one day only accept for publication articles that are accompanied by working software that allows for immediate and reliable reproduction of results.


The factors that led to successful application are: 1.

there is far too much Web content for manual processing; law enforcement personnel should be able to focus on following up leads rather than scouring the Web.


Delanakatrella 003-03 t2 a02js If you need someone to talk to feel free to PM -it really helps to talk to people.

we cannot a priori say that a single linguistic form always indicates a specific stance, but we can find similarities among stances taken with a linguistic form containing it.


One common approach is to aggregate short messages into long pseudodocuments.

it helps users understand the key focus of a microblog conversation.


Though compared with many other tasks in the NLP and information retrieval community, the corpus looks relatively small.

6904333, General Research Fund (GRF) project no.


(DISC) indicate the discourse role of a message; for example, in Figure 1, "How" and the question mark "?"

taking a closer look at the produced summaries, we observe that the system selects messages that contain sentiment-only information, such as "Good point!


• Quotations: This feature identifies whether any quotation appears in the utterance (i.e., replying to another message sarcastically).

for IAC v2 , we found that 25% of the time the sentence with the highest attention weight is also the one picked by the Turkers.


Next, given this sarcastic sentence look back at UserA's post.

for longer posts, often the LSTM models perform poorly because the sarcastic cue is buried under the remaining non-sarcastic parts of the post.


As can be inferred from Figure 7, collecting more tweets seems to have a moderate effect on the overall sentiment analysis performance (91% vs. 94%).

table 11), words that had been discarded were replaced by wildcards ("*").


Networks with ReLU have a lower run time and tend to show better convergence (Talathi and Vartak 2015).

once a BAF has been extracted from text, we can use argumentative reasoning to evaluate the arguments in the BAF and in particular the dialectical "goodness" of an item being reviewed.


Classification of pairs of sentences, amounting to identifying relations between texts, has recently received a great deal of attention.

further, transition applications nodes (TA-nodes) are special kinds of S-nodes connecting locution nodes (L-nodes, special type of I-nodes) to capture dialogue flow.


In the following, we show the effect of adding more labeled data from the MRDA meeting corpus.

the following sequence of equations describe how the memory blocks are updated at every time step t: (1) where U and V are the weight matrices between two consecutive hidden layers, and between the input and the hidden layers, respectively.


The total training loss in the supervised adaptation setting can be written as where the second term is the classification loss on the labeled target data set D l T , and the last term is the discrimination loss on both labeled and unlabeled data in the target domain.

bidirectional RNNs (Schuster and Paliwal 1997) capture dependencies from both directions, thus providing two different views of the same sentence.


By visualizing the type of conversations found by the two models, they show that the output of the HMM+Topic model is more interpretable than that of the HMM one; however, their classification accuracy is not empirically evaluated.

ji, Haffari, and Eisenstein (2016) propose a latent variable RNN that can jointly model sequences of words (i.e., language modeling) and discourse relations between adjacent sentences.


We notice that the two sentences in comment C 4 were mistakenly identified as Statement and Response, respectively, by the B-LSTM conv-glove local model.

we use a log-linear representation for the factors: where φ(.)


Table 13 presents the results for the adapted B-LSTM conv-glove model under the above training conditions (last two rows).

when it comes to speech act recognition in asynchronous conversations, as mentioned before, not many large corpora annotated with a standard tagset are available.


Moreover, these scores are much lower than those from the full data set (from 0.63 to 0.66), suggesting that it may be harder to make fine-grained distinctions (e.g., between two compositional compounds like access road and subway system) than to make inter-range distinctions (e.g., between idiomatic and compositional compounds like ivory tower and access road).

for each parameter, we analyze all possible values of other parameters.


Then, for each fold f i , we exhaustively look for the best configuration (values of WINDOWSIZE, DIMENSION, and WORDFORM) for the union of the other folds (∪ j =i f j ), and predict the 36 compositionality scores for f i using this configuration.

there is no clear tendency in the variation of the standard deviation of the answers and the number of participants n. The values of σ are quite homogeneous, ranging from 1.05 for EN-comp 90 (head) to 1.27 for EN-comp Ext (head).


These results confirm that the predictions are better for compositional than for idiomatic compounds.

we consider the use of filtering techniques, which are used in the literature as a method of guaranteeing data set quality.


LFG utilizes functional schemata that are assigned to phrase structures rather than empty categories to analyze a number of complex linguistic phenomena.

the combination of all y y y k gives enough information to construct y y y.


Our extraction algorithm integrates the key idea underlying LFG and the practice of corpus annotation of CTB.


It starts with k cliques of size 1, which are sorted in increasing order by the amount of missing data they contain.

second, it reflects a concrete technique by which regular sound Greek z u g o n th u g a (ter-) -n u -(os) e r u th (rós) Regular sound correspondences across four Indo-European languages, illustrated with help of alignments along the lines of Anttila (1972, page 246).


This is, of course, not surprising, if we assume that the hypothesis that sound change is an overwhelmingly regular process holds.

input format with the basic values needed to apply the method for automatic correspondence pattern recognition.


If enough words are deleted from singleton patterns, like the ones shown in the examples in Table 6, the method for correspondence pattern recognition will assign them to the same clusters.

this is illustrated in Figure 2 where the cognate sets from table 1 are presented in aligned form, with zero-matches (gaps) being represented as a dash ("-"), and with brackets indicating unalignable parts in the sequences, that is, parts that cannot be aligned, since the differences are not due to regular sound change.


Existing work, including the recurrent neural network architectures proposed by Lowe et al.

the results indicate that both models can significantly outperform the state-of-the-art models.


∀i, GRU encodes the sequential information and the dependency among words until position i in u into the i-th hidden state.

the empirical results are consistent with our theoretical results: SCN is faster than SAN.


Indeed, Bayesian models have been used extensively to uncover complex latent structures behind natural language text in the last decade or so (Goldwater and Griffiths 2007;Griffiths, Steyvers, and Tenenbaum 2007;Goldwater, Griffiths, and Johnson 2009).

we use matrix factorization (Srebro, Rennie, and Jaakkola 2005;Griffiths and Ghahramani 2011) to capture inter-feature dependencies.


The situation is unlikely to change in the foreseeable future for a couple of reasons.

they are complimentary in nature, as SYNDIA outperformed SYN.


All the experiments whose results are reported in the remainder of this subsection have been carried out with an early stopping after 10 epochs if the score on the development set did not increase.

after having defined annotation guidelines and manually tagged a corpus accordingly, as described in the previous section, we report here on experiments on the automatic detection and classification of event mentions.


For example, in the case of 10000, the CG allows both ten thousand and one o o o o, and given the choice, the neural model picks the latter: left to its own devices, the neural model produced one thousand.

rather, neural network solutions in general tend to make unrecoverable errors, and for these we have argued that using trainable finite-state covering grammars is a reasonable approach, but we continue to look for ways to improve covering grammar training and coverage.


8 Similar approaches have been adopted for ASR verbalization (Sak et al.

for example, 45 minutes was verbalized as forty five minute.


As already observed in Example 5, the inside tree decompositions of Section 3 are sub-optimal, in the sense that, regardless of the vertex ordering, they might not be able to provide the smallest possible width for a given graph.

as an example of a leaf node in the tree decomposition, consider node n with span [4,5] and Bag(n ) = {v 1 , v 4 , v 5 }.


This preserves the three properties in the definition of tree decomposition (Definition 1).

with string-to-graph parsing, dependency semantic parsing does not require any hardwired input grammar generating a set of valid graphs.


This shows that inside and outside HRGs with d r = 5 cover 99% of the sentences in the data set.

when we do so, we soon realize that there is a gap in computational complexity between the two cases, because of the structural differences between non-crossing trees and general graphs.


This yields 8,607 combinations, so we represent each language by a 8,607dimensional normalized vector, and compute the cosine distance between these language representations.

3 Our generated trees yield comparable results to previous work (Table 1).


Therefore, we report all further results on the basis of the sim weights.

output: clusters C. Cluster the open neighborhood of u 7: Global Step: Sense Graph Construction 13: C ← Cluster Global (G) Global Step: Sense Graph Clustering 14: Remove the sense labels 15: return C Given a pair {u, v} ∈ E, we reuse the sense information from Table 2 to determine which context of a senseû ∈ V contains v. We denote this as senses , that is, the fact that node v is connected to the node u in the specified sense u senses[u] [v] .


FrameNet is a lexical resource composed of a collection of semantic frames, relationships between them, and a corpus of frame occurrences in text.

this system of broad semantic categories was used by lexicographers who originally constructed WordNet to thematically order the synsets; Figure 14 shows the distribution of the 82,115 noun synsets from WordNet 3.1 across the supersenses.


Finally, for each cluster C i ∈ C, we aggregate the subjects, the verbs, and the objects of the contained triples into separate sets (lines 7-9).

both of the semantic class types used are based on the WordNet lexical database Table 18 Properties of the input data sets used in the semantic class induction experiment compared with the original distributional thesaurus (DT) by biemann and Riedl (2013).


This is to remove rather meaningful triples, for example, (her, make, it), which are, however, present in large amounts in the FrameNet gold standard data set.

we found that they need to be carefully pruned to reduce the error rate of clustering algorithms (Panchenko et al.


During training, a new sense vector is allocated if the current context's similarity to existing senses is below some threshold.

each cluster is transformed into a triframe, which is a triple that is composed of the subjects f s ⊆ V, the verbs f v ⊆ V, and the objects f o ⊆ V. For example, the triples shown in Figure 9 will form a triframe ({man, people, woman}, {make, earn}, {profit, money}).


For Ebeling's method (first column of the Long Memory vertical block), the exponent of the original data set was η = 1.32 for WT2 and η = 1.23 for the PTB, whereas that of both shuffled data sets was η = 1.00, thus indicating no long memory in the latter.

columns 3-7 of Table 2 and Table 3 list the respective results for the scaling properties: Zipf's law, Heaps' law, Ebeling's method, Taylor's law, and the long-range correlation.


As the Simon model had more new words later in a sequence, the correlation stayed large even for two sequences with a large distance between them.

§8 discusses evaluation of GAN models for text generation.


On average, metrics do a better job at evaluating high-quality MT, whereas low-quality MT evaluation appears to be more challenging.

the metrics tested in this work achieve a higher correlation with human assessments (and therefore, are more reliable) when evaluating the outputs of neural Mt systems.


This is not surprising because, as mentioned before, multiple confounding factors are involved, including the difficulty of predicting a particular type of judgment, the overall level of MT quality, the reliability of manual assessments, and so on.

the metrics in tables 3 and 4 are divided into three groups, as discussed in Section 3.


This section presents the various data sets used in our meta-evaluation study.


Consider the following two examples from the WMT16 DA data set.

the results in Table 10 should not be compared directly but rather in relative terms, that is, by seeing whether the ordering of the metrics based on the correlation with human assessments is similar for different data sets, annotated with different labels.


In addition to being psychologically motivated, this sort of gradient representation is also more compatible with machine learning algorithms and particularly with deep neural models that naturally operate with real-valued multidimensional word embeddings and hidden states.

establishing which of the above-mentioned strategies is optimal in terms of prediction accuracy is not straightforward.


We start this survey with a brief overview of Linguistic Typology ( § 2) and multilingual NLP ( § 3).

although binarization helps harmonizing different features and different databases, it muddies the different types of typological variables.


In fact, they have different weaknesses: Language identity (and consequently typology) fails to abstract away from language scripts.

in practice, learning algorithms, both linear (e.g., structured perceptron [Collins 2002], MiRA [Crammer and Singer 2003] and structured support vector machine [Taskar, Guestrin, and Koller 2004]) and non-linear (deep neural models) iterate between an inference step and a step of parameter update with respect to a gold standard.


It is hard to assess at a large scale how informative a set of typological features is.

the most widespread approach to exploit typological features in NLP algorithms is "selective sharing" for language transfer.


Finally, we highlight some limitations of database documentation with respect to coverage and feature granularity, and discuss how missing and finer-grained features can be obtained automatically.


In this section, we have briefly outlined the most widely used methods in multilingual NLP.

semantic typology studies languages at the semantic and pragmatic levels.


But both are seen as models, in the mathematical sense, for type-specific collections of constraints.

we expected to assemble the separate components into a prototype system, after a few years of work.


We use strict as well as relaxed comparison.

the axiom can be deleted by labeling all its discourse elements as O.


Discourse analysis has been shown to be useful for many NLP tasks, such as question answering (Chai and Jin 2004;Lioma, Larsen, and Lu 2012;Jansen, Surdeanu, and Clark 2014), summarization (Louis, Joshi, and Nenkova 2010), and information extraction (Kitani, Eriguchi, and Hara 1994).


our base parser identifies the premise and conclusion portions of each axiom and then uses GEoS's text parser to parse the two portions into a logical formula.


This helps the readers digest the material quickly and much more easily.

luc, Mojahid, and Virbel (1999) study enumerations.


This needs to be understood by context as it defies the standard type definition of an angle.

can these text formatting features be useful in an information extraction system too?


We achieve slight improvements by combining n-grams with different sizes (i.e., if n = 3, we use 1-grams, 2-grams, and 3-grams combined).


First, we address the task of identifying cognates.

we use several orthographic metrics widely used in this research area: the edit distance (Levenshtein 1965), the longest common subsequence ratio (Melamed 1995), and the XDice metric (Brew and McKelvie 1996).


Moreover, these works only focused on single-domain extraction, which failed to adapt well to other domains of interest where only unlabeled data are available.

2010;Bollegala, Maehara, and ichi Kawarabayashi 2015;Yu and Jiang 2016).


compared with our preliminary work (Wang et al.


We use the same word embeddings as our proposed models.

when feeding an instance x either from the source domain or target domain, the model will generate the following two outputs: with y and d indicating the main prediction and domain prediction, respectively.


To reduce the negative effect brought by noisy relation labels generated from imperfect parsers, an auto-encoder is incorporated to generate relation clusters y G ij as an encoding step.

the conditioned information refers to the upward dependency relation for each word.


The objective of learning a DAN is twofold: (1) The model should be discriminative for the main task in the source domain.

figure 8 provides the average performance over six source-target experiments for varying β and the number of clustered relation group |G|.


These systems leverage POS taggers and dependency parsers to extract relation tuples and are in line with our method for data generation.

charley speed has shoe size uk 9.5 -us 10 -eu 44 and his an agency of Models 1 Agency JAM Agency.


The importance of contextual domain knowledge highlighted by this example was first explored by Saint-Dizier (2017) where, via the analysis of various corpora, the types of knowledge that are required to develop an efficient argument mining system are explored.

this is a substantially easier task once the scheme type is known.


The examples are mere illustrations that are meant to help the reader interpret different LDN values, including the 0.51 cut-off.

we now have a principled way of distinguishing languages and dialects.


Early application of sequential morphotactic constraints during the segmentation process avoids an excessive number of meaningless segmentation possibilities before the computationally more expensive unification process.

the output of the segmentation phase is very rich, as shown in Figure 2 with the word amarengan (Eng.


Fig.1 illustrates a sample of noun clusters between curly brackets projected from a set of SIs, together with a list of the verbs found in the same SIs the su x`S' stands for subject, and`O' for object.

noun clusters of Figs.1 and 2 are admittedly considerably ne grained.


General dictionaries testify all possible senses of a given word; typical word collocates acquired from dictionaries tend to cover the entire range of possible senses of a headword.

there is wide room for further research in this area, but truly explorative tools are still needed.


Here, compatible" means that the tree represented by s can be adjoined or substituted into the tree represented by s m , according to the XTAG grammar.

we use two metrics suggested in the MT literature Alshawi et al., 1998 based on string edit distance between the output of the generation system and the reference corpus string from the wSJ.


The grammatical analysis itself thus remains completely declarative and only makes use of unification.

it is designed for unification-based grammar formalisms with typed feature stuctures as in HPSG and is not restricted to simple morphosyntactic features.


In order to process unknown word forms, we postulate canonical lexical entries which are returned by lexical lookup if a word is not recorded in the lexicon.

only agreement information from a context, such as der enge Sund 'the narrow strait' (nominative), can establish the gender of Sund as being masculine.


The proposed parse is incorrect since it has the incorrect feature value for the TENSE attribute.

for our experiments in section 3.2, we used a sample size of N = 10,000 derivations which corresponds to a maximal standard error σ of 0.005 (σ ≤ 1/(2√Ν), see Bod 1998).


On the WSJ this led to a decrease in LP/LR of 1.2%/1.0% for probabilistic DOP.

this non-probabilistic DOP model parses each sentence by returning its shortest derivation (consisting of the fewest subtrees seen in the corpus).


"All documents with 1 digest sentence" represents documents whose digests are longer than a single sentence; "All documents whose 1st sent is not in target digest" extracts a document set for which a baseline strategy automatically picking a representative sentence for inclusion in the summary would be inappropriate.

still, in the right operational environment even very simple heuristics-take the first sentence from each segment, for instance-have remarkably noticeable impact.


Additional 15.2% of failures could also be avoided if the summarizer was capable of detecting sub-stories within a single section, leading/trailing noise (see below), and so forth.

these elaborate the intuitions underlying our approach to leveraging lexical cohesion effects (see Section 1.2).


UNL documents can be processed by indexing, retrieval and knowledge extraction tools without being converted to natural languages.

we want our parser to produce what we consider a correct syntactic representation of the sentence -first of all because we believe that this interpretation is a true fact about the natural language.


In the pre-nominal construction the relation between the target noun phrase and quantifier is explicit.

the semantic classes are ordered so that the most typical use comes first.


On the one hand, if one disregards step (ii) (a disguised recency heuristic spuriously mixed with binding constraints) and considers the result of verifying binding constraints to be the assignment to an NP of the set of indices of its antecedent candidates, then it is possible to discard the proliferation of indexed trees as a way to express anaphoric ambiguity.

johnson's algorithm is embodied in Prolog code.


Other applications of instrumentation are possible; genre adaptation was discussed in some depth.

he eats the schnitzel naked raw quickly.'


Note that the word order on the target-language side is not considered when building the term vector, so it need not be the same as on the source-language side; the examples were chosen with the same word order merely for clarity.

figure 3 shows how adding automaticallygenerated equivalence classes substantially increases the coverage of the EBMT system.


In the example, the distance finally associated with sense number 0.I.4 is 0.6625, which is smaller than the one associated with sense number 0.I.3 (0.675).

even if there are no matching rules, the system gives as result the most probable meaning of the word to disambiguate.


This is a basic di erence that explains why WSD tasks usually achieve better performance than term categorization and thesaurus extension.

the two issues can be di erentiated with respect to the manipulated data.


Because each MU is assigned a unique ID, the links among MUs form the answer keys for the performance evaluation.

word segmentation is another source of errors.


In this model, users can read both the common views and different views of reporters.

the country name is important in foreign news.


A similar pattern holds for negative adaptation, but in the reverse direction.

we applied this procedure to a year of the AP news and found a sizable gain in information on average: 0.75 bits per word type per document.


To overcome this, we propose to de ne a probability model in terms of senses from a semantic hierarchy, exploiting the fact that senses of nouns can be grouped together into semantically similar classes.

there is also a development set to prevent implicit training on the test set during development.


5 We compared 5 Fisher's exact test could be used for tables with low counts, but we do not do so because tables dominated by l o w counts are likely to have a high percentage of noise, due to the way counts for a noun are split among the performance of log-likelihood 2 and Pearson's 2 using the PP-attachment experiment described in Section 5.

knowledge of selectional preferences cannot by itself solve the problem of structural disambiguation, and this further work will also look at using additional knowledge, such as subcategorisation information.


In most cases, the COLLECT module determines an LPA by enumerating all antecedents in a window of text that precedes the anaphor under scrutiny (Hobbs, 1978;Lappin and Leass, 1994;Mitkov, 1997;Kameyama, 1997;Ge et al., 1998).

for example, for the Linear-2 model and referential expression the smaller company in unit 9, we estimated whether a co-referential link could be established between the smaller company and another referential expression in units 7, 8, or 9.


The method described in section 3.2.1 estimates the potential of Linear-k and Discourse-VT-k models to determine correct co-referential links by treating EDRAs as sets.

had unit 5 been Mr. Cassey succeeds M. James Barrett, 50, we would have had to go back 8 units (to unit 1) in order to correctly resolve the RE the smaller company.


The antecedent Genetic Therapy, Inc. appears in unit 1; therefore, using VT we search back 2 units (units 8 and 1) to find a correct antecedent.

(1999) with discourse structures built in the style of Mann and Thompson (1988).


VT extends and formalizes the relation between discourse structure and reference proposed by Fox (1987).

to resolve the same reference using a linear model, four units (units 8, 7, 6, and 5) must be examined before Genetic Therapy is found.


536-620) call 'ellipsis in the strict sense'; they view ellipsis as a purely surface phenomenon: the recoverability of the ellipted words is always unique and 'fits' into the surface structure.

there is no such ambiguity in (26 'To Peter, nobody gave a book and to Mary, flowers.'


Whereas (Gundel et al., 1993) do not attempt to make their focus notion operationalizable, this has been attempted by further developments of centering.

besides the well established problem of pronoun resolution, pronoun generation is now attracting renewed attention.


These results prove the validity of our adjustment ratio-based rule selection framework.

again, this kind of invalid rules would not make a significant difference in the final output of the parser.


This section introduces the selective left-corner transform and two additional factorization transforms which apply to its output.

notice that the top-down predicted category D is passed down this right-recursive chain, e ectively multiplying each left-corner productions by the possible top-down predicted categories.


Tables 1 and 2 give the sizes of selective leftcorner grammar transforms of G for various values of the left-corner set L and factorizations, without and with epsilon-removal respectively.

with a left-recursive grammar such parsers typically fail to terminate.


The mmsubcat:end: feature provides access to the end of the current mmsubcat: list.

there are however multimodal utterances where a single gesture maps onto a higher level of constituent structure in the spoken language parse.


more recursive semantic phenomena such as possessives and other complex noun phrases are added to the grammar the resulting machines become larger.

the next step is to generate the combined meaning representation.


We have presented here a novel approach to multimodal language processing in which spoken language and gesture are parsed and integrated by a single weighted finite-state device.

an FST can be regarded as a 2-tape FSA with an input tape from which the input symbols are read and an output tape where the output symbols are written.


MT Becoming fearful, the child grasped the arm of the father tightly  Figure 1 shows a range of output quality.

in the case of "Low Density Machine Translation" (see Nirenburg andRaskin 1998, Jones andHavrilla 1998) commercial market forces are not likely to provide significant incentives for machine translation systems for Low Density (Non-Major) languages any time soon.


Unknown words pose a significant recall problem in languages that don't place spaces between words.

many such items are preferably treated as lexical entries because In addition, many Japanese verbs and adjectives (and words derived from them) have a variety of accepted spellings associated with okurigana, optional characters representing inflectional endings.


These characteristics imply the following two advantages.

• The Pair Model outperforms the W/O Restriction Model by 0.3%.


The main issue in this type of research is how to assign likelihoods to a single linguistic structure generated by a grammar.

instead of learning two output values "T(true)" or "F(false)" for the dependency between two bunsetsus, three output values are used for learning: the bunsetsu i is "bynd (dependent on a bunsetsu beyond j)", "dpnd (dependent on the bunsetsu j)" or "btwn (dependent on a bunsetsu between i and j)".


As English words and Korean transliterations are usually technical terms and proper nouns, it is hard to nd a transliteration and its variations in a dictionary.

but it is not feasible because transliterated words are usually technical terms and proper nouns that have rich productivity.


Or we can simulate a left-corner enumeration of the search space, considering categories that are reachable from a current goal category and match the left corner of a possible rule.

without loss of linguistic relevance, we concentrate here on a restricted class of LFG grammars, those that assign acyclic f-structures to sentences.


In Japanese, case structure analysis is very important to handle several troublesome characteristics of Japanese such as scrambling, omission of case components, and disappearance of case markers.

as shown in Table 3, the accuracy of the analysis was fairly good: that for topic-markers was 82% and that for clausal modifiers was 73%.


The experiment results showed that with respect to performance, the difference between the proposed method and the method by supervised learning is insignificant.

other verbs and adjectives are not informative in many cases.


A simpli ed version of one such procedural text is given for English in Figure 1.

in Russian, in contrast, zero-or-three dimensional objects preposition: v are opposed to one-or-two-dimensional objects preposition: na.


The analogical derivation, noted`0 0 is dened in the following way: Although we use the notation !

8(A;B;C;D) 2 (V 3 ) 4 ; A : B = C : D ) jAj + jDj = jBj + jCj Another intuitive idea about analogies between strings of symbols is that two analogies could always be concatenated.


Our feeling is that analogy between strings of symbols is an operation as fundamental as, e.g., addition is to natural numbers.

for recognition, the strings in the pairs of M are used in the reverse order they appear in M, and the analogies are solved in the other direction than for generation.


In order to support the next hypothesis we will make on analogies on strings of symbols, let us list a small number of analogies in English: hypothesis : hypotheses = thesis : theses leaf : leaves = calf : calves give : gave = sing : sang inexact : exact = incapable : capable plus some true analogies but with no meaning in language: aa : aaaa = aaaa : aaaaaa 2 give : gave = bid : bad walk : walked = go : goed 3 and some counter-examples (noted with 6 =): aaaa : bbbb 6 = cccc : dddd 4 dfhka : bzvmbz 6 = bzvmbz : dfhka 2 This analogy holds independently of the truth (or falsity of) aa : aaaa = aaaa : aaaaaaaa ( a 2 : a 4 = a 4 : a 8 ).

a language like fa 2 n =n 2 INg is not a language of analogical strings, as it does not have the bounded growth property.


Note that at some states it is easy for a human to make the correct action choice.

please say an activity name from this list.


A more standard alternative would be comparison to the very best hand-designed xed strategy.

then we computed the optimal dialogue strategy in this MDP using RL cf.


Finally, a companion paper Singh et al., 2000 shows that the performance of the learned strategy is better than several standard" xed strategies such as always use system-initiative and no-con rmation.

even when using the learned strategy, NJFun still sometimes chooses randomly between certain action pairs.


when word w is given in position x (x = t0l; 1 1 1 ; t +r), element ipt x of input IPTi s a w eighted pattern, dened as ipt x = g x 1 (e w1 ; e w2 ; 1 11; e w ); (4) where g x is the information gain which can be obtained using information theory (for details see Ma and Isahara, 1998) and is the number of types of POSs.


OPT), where w, w 1 , and w 2 are words and is the POS.

the training is therefore performed step by step from small networks to large ones (for details see Ma, et al.


A number of researchers have already shown how individual rhetorical and intentional judgments can be derived automatically from linguistic constructs such as tense and aspect, certain patterns of pronominalization and anaphoric usages, it-clefts, and discourse markers or cue phrases.

the intention associated with segment A 2 ; B 2 is a function both of its nuclei, A 2 and B 2 , and of the rhetorical relation of CONTRAST that holds between them.


The algorithms are simple to implement by specifying the inference rules as constraint propagation rules, the axioms and the goal items as constraints.

these systems with Det the Figure 1: Example Grammar: PP-attachment maintain a constraint base or store which is continually monitored for possible rule applications, i.e., whether there is enough information present to successfully use a rule to simplify constraints or to derive new constraints.


When multiple rules have the same probability and similarity, the method takes the examples used by the rules having the highest probability and the highest similarity, and chooses the category with the larger number of examples as the desired answer, in the same way as in Method 1.

our bunsetsu identication method uses the morphological information of the two preceding and two succeeding morphemes of an analyzed space between two adjacent morphemes.


Thanks are due to Lynne Cahill, Roger Evans and Neil Tipper for stimulating cooperation within the CLIME team at the University of Brighton.

the networks which we have used are an extension of commonly used OO networks for natural language generation.


As we stated in the previous section, we wish to see whether DOT's performance increases as we vary the maximum width of a tree.

the probability of a translation w s µ w t is the sum of the probabilities of its derivations: The justification of this last equation is quite trivial.


unwelcome but sometimes necessary), it is penalized, by a soft constraint, during a subsequent e v aluation phase in which the enumerated solutions are ordered from best to worst.

part of the interest of the problem is that RS and TS are not always isomorphic this will be illustrated later by an alternative TS realizing gure 1 ( gure 6b).


The solution in gure 2, for example, h a s n i n e T S n o d e s , e a c h bearing a text-level variable di erent realizations of the same RS might have m o r e n o d e s , o r f e w er.

our goal in iconoclast has been to explore the huge variety of ways in which an RS can be conveyed, noting stylistic reasons why o n e v ersion might be preferred to another, with the eventual aim of providing a system in which the user enjoys negrained control over sty l e a s w ell as content.


ft takes a node as input and returns a path line 1.

the second step is to create the word graph by identifying words from character using a dictionary.


Tokenizers designed for those languages are generally very tied to a given system and language.

sumo is a formalism for universal segmentation of text.


In 20 25,`more' made a determiner when supplied with an appropriate`than'-phrase.

there are various reasons for moving things around in this way moving`who' to the left in 1 provides an easy way of picking out the boundary of the relative clause; moving`the main course' and`in the park' in 2 and 3 puts them into thematically informationally more prominent positions; and moving the sentential complement`that she loved me' to the right in 4 reduces the attachment a m biguity that arises in the alternative form.


Rules and lexical entries were added in small groups, typically 2 3 rules or 5 10 lexical entries in one increment.

a three-words-per-category grammar could not produce successful speech recognition.


This means that the dependency links establish a tree structure, where each node is labeled by a n ucleus.

note the use of V = 0, rather than a partitioning of the labels into terminal and nonterminal symbols, to indicate that is a leaf node.


For example, from the occurrences of the transitive verb absolvovat ("go through something") that occurred ten times in the corpus, no occurrence consisted of the verb-object pair alone.

our technique has to deal with a different kind of data as compared to previous approaches.


Prosecutors repeatedly asked Secret Service personnel to repeat gossip they may have heard.

in this group, we had: The intuition for the second group is that a topic flows from one grammatical unit to another so that the salient nouns, particularly the surface subjects, in successive clauses should reveal the associations we are seeking.


This paper presents our work on realizing expressions of doubt appropriately in natural language dialogues.

the subjects were asked to choose and rate the strength of those that were communicated by the doubting agent in order to see if these correlated with the endorsements from which they would have been generated.


Since autonomous agents enter the dialogue with di ering domain, world, and personal knowledge, it is inevitable that some beliefs conveyed by an utterance will not be accepted because they conict with existing beliefs of the agent.

expert -Belief supported by expert testimony, or thought to be common knowledge among experts in this domain.


I proposed two algorithms for automatically classifying verbs semantically, based on their alternation behaviour.

some WordNet classes were not chosen at all, e.g.


So if this is the output, we don't record the edge as it is the default.

a large corpus is parsed by the analyzer, and the results are added to the training corpus.


There may be several possible lexical units for one UW.

emphasis is on a very large lexical coverage, so that all groups spend most of their time on the UNL-NL lexicons, and develop tools and methods for efficient lexical development.


That was a main cause of errors and declines.

we hope to overcome it by a model that classies noun phrases, for example using verb-noun or adjective-noun relations.


Unlike the former approach, this approach extracts various types of local patterns at the same time.

the former retrieves words and phrases with a designated syntactic category while the latter retrieves that with various syntactic categories at the same time.


Owing to these features, we can easily apply the retrieved technical terms to a real MT system.

we consider it not to be critical under the specic condition that the variety of input patterns is very small.


The appeal of using definitions as corpora for alignment is founded on two reasons.

of applying the clustering program to the 387 strings, it is evident that the majority of clusters were related to "measure" and "instrument".


Then, short and long strings should be treated differently in the disambiguation process.

string frequency is also useful information for our task.


The removal of the pass feature appears to improve performance row 3 o f T able 4.

appendix a Unergatives: oated, galloped, glided, hiked, hopped, hurried, jogged, jumped, leaped, marched, paraded, raced, rushed, scooted, scurried, skipped, tiptoed, trotted, vaulted, wandered.


Attaining a high accuracy using fully stochastic methods is particularly difficult for Japanese due to the prevalence of orthographic variants (a word can be spelled in many different ways by combining different character sets), which exacerbates the sparse data problem.

since the parser currently uses extremely crude heuristics to rank multiple parses, the first analysis was chosen as the best word-breaking analysis in this experiment whenever the parser returned multiple parse analyses.


The positive e ect of system combination for non-language processing tasks has been shown in a large body of machine learning work.

each combination method outperformed the best individual learning algorithm and a majority vote of the top ve systems performed best.


We h a ve used this bracket representation O+C as well.

the results presented here were obtained by optimizing MBSL parameters based on 5-fold CV on the training data.


pointed out that summaries made by this method tend to be burdensome to read, and have proposed phrase-representation summarization as an alternative.

most summarization studies (including Zechner, 1996) are based on important sentence selection and seek better selection methods.


This means that the cooccurrence probability between implicit terms and verbs are also important in eliminating inappropriate nouns.

it is not concerned with the semantic relation between an explicit term and implicit term, because such semantic relations are not directly expressed in corpora, i.e.


Previous systems, however, collect such translation patterns mostly manually.

we have also developed a GUI system with which a user can check and correct the correspondences retrieved by the system.


Since Imaichi's algorithm considers only Japanese in which all words other than the last word modi es one of the succeeding words, it does not deal with cases usually seen in European languages where a word modi es one of the preceding words.

... and a rule which has two o r more RHS terms and the leftmost RHS term is not a head denoted by fX !


The graphs in Figure 6 indicate the reduction ratios of active arcs, inactive arcs, and processing time for using conditions for total dependency information and conditions for partial dependency information.

it is not applicable to any language other than Japanese in general.


Taking the annotated dialogs as a whole, 21.4 of all pronouns have a non-NP antecedent, and 27 do not have an annotatable antecedent at all.

this algorithm encompasses both personal and demonstrative pronouns and exploits their contrastive usage patterns, relying on syntactic clues and verb subcategorizations as input.


We chose a spoken dialog corpus that includes demonstrative pronouns because demonstratives are both a prominent feature of problem-solving dialogs and a sorely neglected field of study.

our data is taken from the TRAINS93 corpus of humanhuman problem solving dialogs in the logistics planning domain.


Then, the two non-terminals are merged as described in section 3.1.3.

the two phases in ABL are closely related to some previous work.


We present results of several evaluations of our system using human transcripts of spontaneous telephone conversations in English and Spanish from the Callhome corpus LDC, 1996, in particular the accuracy of the topic segmentation and information condensing components sections 6 and 7.

since Callhome dialogues are always multi-topical, segmenting them into topical units is an important step in our summarization system.


We divided the subjects in eight groups such that no subject had to perform more than one task on the same text and we distributed the di erent tasks evenly for each group.

4 Table 4 shows the results from these experiments.


The lower overall precision of the test set 2 (about 2% lower than test set 1) also indicates the boundary prediction difficulties of the conjunction structures, because there are more CSs in test set 2 than in test set 1.

due to the long distance dependencies among them, it's very difficult to assign the correct boundary tags to the words in these structures only according to the local context templates.


We could enforce appropriateness in a typed feature setting, for example, by expressing further regular constraints over paths.

many diagram classes require greater than context free power for their adequate description.


An austere theory merely relies upon simple recognition of the shape of the concepts.

a system with high quality and restricted coverage might b e useful for in-house MT-applications or a controlled language.


We use about 1500 sentences for training and 591 independent sentences for evaluation.

forward Application A : X=Args f Yg Y = X=Args Backward Application A : Using these rules, a verb can apply to its arguments in any order, or as in most cases, the case-marked noun phrases, which are typeraised functors, can apply to the appropriate verbs.


In general, a statistical parsing model de nes the conditional probability, P jS, for each candidate tree for a sentence S. A generative model uses the observation that maximising P ;S is equivalent to maximising P jS 6 .

pOSpart-ofspeech tagger which is tightly coupled with the morphological analyzer removes the irrelevant morpheme candidates from the morpheme graph.


in the coordination relation, it is easier to merge two medium sized trees.


The summaries are informative in nature.

the extraction method described here is based on the Mikrokosmos ontology (Mahesh, 1995), and uses the concepts in the ontology both to define an extraction template and to control the extraction process.


We address this problem by employing compositional semantics and the adjunction of syntactic phrases to support regular verb sense extensions.

verb classes allow us to capture generalizations about verb behavior.


The extensive effort put into design helps to ensure that the product focuses on errors people actually make and care about.

such documents are extremely hard to come by, so we found ways to have such unedited text data specifically created for our project.


During the development process, testers give feedback and quality assessment, based on both the blind and non-blind corpora, and using a variety of tools to provide quick turn-around after a change to the system.

many of the grammar mistakes native users make are alsoor even more -common among non-native users, so we know that the grammar checker will be helpful to this population as well.


In addition, a greater use of syntactic information will require more computation for nding a more detailed syntactic parse.

korean is a post-positional agglutinative language.


The evaluation of the natural language subsystem is designed to test whether reasonable translations are contained within this set of words.

the error rate of paced speech can be as low as 5% if the vocabulary is severely limited or if the text is highly predictable and the system is tuned to that particular genre.


Traditionally, database management systems were designed to store snapshot information, valid at a particular moment of time state.

during some month every year opens up exponential scoping possibilities, some choices are eliminated by world knowledge, which is encoded in the grammar e.g.


The addition of the temporal dimension to database systems increases their power but also their complexity.

it is our belief that such users are bound to nd the expression of complex temporal queries in SQL Temporal to be extremely di cult.


abstr_rel with subject you The remaining 2 are due to errors in the SC module, errors in the tree bank, or coordination problems between SC and tree bank.

the process of SC can be split into two phases see Figure 4.


In these cases, underspecication should be used to defer the resolution of choices.

usually an abstract predicate is incorporated which gives a hint to subsequent modules that aim to piece together partial utterances.


The best fully automatic system was successful in extracting most of these specific patterns.

at the evaluation, participants were asked to identify NE expressions as correctly as possible.


It would be a clear advantage to have a methodology that could provide a much cheaper way of NLP resources acquisition.

every SL clause element is now tagged with certain values of diagnostic and restricting tags.


Two sets of data were created for testing and comparison: Generic Corpus (GC) and Domainspecific Corpus (DC).

n-gram probability is approximated by bigram or trigram estimates.


This can be used as an alternative to phonological/phonotactic conditioning or in addition to it, for just those cases where there is more than one correspondence but no obvious phonological/phonotactic conditioning for the decision between phonemes.

even if only half or fewer of the Dutch words, for example, have the same vowel in cognates for which the english words have the same vowel, this still means that those half can be defined without the need for the language-specific vowel to be defined.


sophisticated analysis, phonological and phonotactic information would need to be considered as well.

), we might be able to generalise over these three language-specific phonemes and introduce a metaphoneme, e.g.


Another LTG tool, sgrpg is more powerful, but its control les have in our opinion a non-intuitive and complex syntax 3 .

if a given variable $a contains a list of elements f A, B, C, ...g, transforming $a will apply the transformation in sequence to A , B , C and so on.


T 1 ← Ψ −1 (T 0 ) (consists of reinserting in T 0 the transitions of T which where removed).

with our new approach, we do not have to take into account the number of transducers R i and their auxiliary labels as was the case with the approach used before.


Unfortunately, it is infeasible to enumerate all the paths in a cyclic transducer.

output: is an unambiguous transducer, X 1 is the set of auxiliary symbols.


On the other hand for the non-hapax pairs the DIST condition was successful in more than 85% of the cases.

the hypotheses-testing methods such as Gale and Church (1991), Smadja et al.


We can combine the subtree to restore the fanout flexibility using the production  Table 1 shows some bilingual bracketing examples obtained using the above method.

chinese: Using SBTG, the bilingual bracketing result is : The result is not consistent with the expected syntactic structure.


Here, "a" denotes the probability of syntactic rules.

the violate matches acquired in bilingual parsing are mainly due to the empty word alignments.


Since we did not have access to any larger treebank for German, we used cross validation on the CZ test set in a third experiment.

they can be far apart from each other, the only restriction being that they cooccur within the same clause.


Syntagmatic associations are words that frequently occur together.

please note that this method is computationally far more efficient than the computation of paradigmatic associations.


It is expected that the most similar words are ranked first in the sorted list.

we have not yet conducted a systematic quantitative evaluation.


Especially, the translation process will be unavailable if there is a lack of sufficient anchor texts for a particular language pair.

there are still some problems need to be further investigated in the future.


1 Here "Cognate" mainly refers to those English proper nouns (such as those company names of IBM, HP; or the technical terms such as IEEE-1394, etc.)

each Chinese-Lexicon obtained from the dictionary is first augmented with its associated Chinese characters, and then the augmented transfer-lexicons set are matched with the target Chinese sentence(s).


Different with the existing approaches which align hierarchical structures like sub-trees, our method conducts alignment on chunks.

b) The HIT Treebank 2 , containing 2000 sentences.


For example, the ORG " Ñ Ñ þ N ( Ì " (Air China Corporation) contains the LOC "Ñ" (China).

there is no space to mark the word boundary and no standard definition of words in Chinese.


An intermediate step may be to mark nominal, verbal etc.

we have tentatively set the limit to be ¤ ¤ , as a reasonable compromise in our system between throughput and accuracy.


The method exploits the idea of relating correspondences in bilingual wordlists to translational equivalence associations in bitexts through the employment of models developed in the context of statistical machine translation, The second task addressed in this paper is the identification of cognates on the basis of the discovered correspondences.

the main idea is to induce a model of sound correspondence in a bilingual wordlist, in the same way as one induces a model of translational equivalence among words in a parallel corpus.


(Cheolsu who has big eyes) In example 6, the context NP is 'nun' and its functional word is 'i' which may represent that it is subject of 'keu-da', thus system may wrongly determine that 'Cheolsu' is not a subject of 'keu-da' because the subject of 'keu-da' has been made with 'nun'.

in order to maximize the distance, we should minimize ||w|| with the condition that there are no data points between H 1 and H 2 , The SVM problem is to find such w and b that satisfy the above constraints.


This adnoun clause is called a relative adnoun clause whose complement moves to the NP modified by the adnoun clause and the NP modified by a relative adnoun clause is called a head NP.

both 'Cheolsu' and 'nun' are the subjects of 'keu-da'.


(Gale and Church, 1991) has used the φ 2 statistics as the correspondence level of the word pairs and has showed that it was more effective than the mutual information.

japanese sentences were parsed by KNP 1 and English sentences were parsed by Apple Pie Parser 2 .


For features (1a), words included in a candidate of the translation pair are looked up with the translation dictionary and the bilingual word pairs in the candidate become features.

for the optimal parameters λ * = arg max λ D, each training sample x i where λ * i > 0 is corresponding to support vector.


If the training samples can be separated linearly, there could exist two or more pairs of w and b that Figure 1: A separating hyperplain satisfy equation 1.

the method proposed in this paper does not require aligned parallel corpora which do not exist too many at present.


The method proposed in this paper does not require aligned parallel corpora which do not exist too many at present.

for features (1b), all pairs of words which co-occurred with a candidate of the translation pair are looked up with the translation dictionary and the bilingual word pairs in the dictionary become features.


researches of extracting translation pairs from parallel corpora automatically become active recently (Gale and Church, 1991;Kaji and Aizono, 1996;Tanaka and Iwasaki, 1996;Kitamura and Matsumoto, 1996;Fung, 1997;Melamed, 1997;Sato and Nakanishi, 1998).


As an example of such an as yet "undetectable" error in German we might take the configuration where two words tagged as finite verbs are separated from each other by a string consisting of nouns, adjectives, articles and prepositions only.

on the search for pairs of adjacent tags which constitute an incorrect configuration in a text of a particular language (in English, e.g., the bigram ARTICLE-FINITE VERB).


These results show that there was only a negligible (and, according to the χ 2 test, statistically insignificant) difference between the results in the cases when the tagger was both trained and tested on "old" corpus and both trained and tested on the "corrected" corpus.

in case one of the tags Middle_1, Middle_2 occurs already in the set Possible_inner_Tags, no action is to be taken, but in case the set Possible_inner_Tags contains neither of Middle_1, Middle_2, both the tags Middle_1 and Middle_2 are to be added into the set Possible_inner_Tags.


When aiming at correction of errors in general, the basic condition which is to be met is that the seem, it is in fact not met fully satisfactorily in any tagset we know of; for more, cf.

obviously, quite a number of errors remain undetected by such a strategy.


In the worst case: the icons are all predicates; no possible role/filler allotment in the whole sequence is below the threshold of acceptability.

since no morphological clue is available either to tell that an icon is, e.g., accusative or dative, we have to rely on semantic knowledge to guide role assignment.


The probability distribution that satisfies the above property is the one with the highest entropy.

we will refer to our system as MENERGI (Maximum Entropy Named Entity Recognizer using Global Information).


Next, the paper proceeds to describe in more detail the anaphora resolution module.

the bracket crossing accuracy is 80%, which is comparable to some probabilistic approaches.


"Aspects are different ways of viewing the 4 It is controversial whether to include "futurity" within the aspect category.

the two types of aspect are incompatible and cannot combine.


More precisely, the combination of these two factors is achieved by a geometric mean: where coh(w,w i ) is the measure of the cohesion between w and w i in the collocation network.

this algorithm is completed by a specific mechanism related to the OutOftopic state.


Depending on the complexity of user inputs, the representation can be composed by a flexible combi- For example, U4 in Figure 2 is a complex input, where the speech input "what about houses with this style around here" consists of multiple objects with different relations.

based on the domain context that a city cannot have a feature Style, MIND determines that the deictic gesture is actually resolving the refer- ence of "here".


Inspired by earlier works on multimodal interfaces (e.g., Bolt, 1980;Cohen el al., 1996;Wahlster, 1991;Zancanaro et al., 1997), we are currently building an intelligent infrastructure, called Responsive Information Architect (RIA) to aid users in their information-seeking process.

relation indicates the temporal relations (e.g., Precede or Succeed) or ordinal relations (e.g., first).


From the gesture input, only one Attention structure is generated which corresponds to the city of White Plains.

mIND uses contexts to improve alignment based on our semantics-based representation.


The author would like to thank Shimei Pan and Michelle Zhou for their contributions on semantic models.

this deictic gesture could either refer to the town of Irvington or houses.


In the multi-step movement approach, the question to model for each potential attachment site of an extraposable clause is whether the clause should move up to its grandparent (a "yes" answer) or remain attached to its current parent (a "no" answer).

the two approaches differ in their view of the movement operation: one approach models multi-step movement through intermediate nodes to the ultimate target node, while the other approach models one-step movement to the target node.


We think that there are two main parameters affecting the grouping result: the weight of the syntactic components and the threshold for the similarity measurement in the similarity-based method.

for example, sentence (1.5) would be grouped together with the above example sentences because it poses the same request: buy books; and sentences (1.6) to (1.8) would be in one group because they are all about price information.


The French examples 14and 15 The relevant difference in this case is in the postnominal modifier: in 14, the postnominal adjective is syntactically simple, containing no syntactic dependents of its own, while in (15) the postnominal participial clause is complex, containing a complement prepositional phrase.

the generation component of the Mt application could then make use of scope information, perhaps in addition to scope-independent ordering conventions (Malouf, 2000), to generate the modifiers in the correct order.